Biblio

The analysis shows how important Power Network Measuring and Characterization (PSMC) is to the plan. Networks planning and oversight for the transmission of electrical energy is becoming increasingly frequent. In reaction to the current contest of assimilating trying to cut charging in the crate, estimation, information sharing, but rather govern into PSMC reasonable quantities, Electrical Transmit Monitoring and Management provides a thorough outline of founding principles together with smart sensors for domestic spying, security precautions, and control of developed broadening power systems.Electricity supply control must depend increasingly heavily on telecommunications infrastructure to manage and run their processes because of the fluctuation in transmission and distribution of electricity. A wider attack surface will also be available to threat hackers as a result of the more communications. Large-scale blackout have occurred in the past as a consequence of cyberattacks on electrical networks. In order to pinpoint the key issues influencing power grid computer networks, we looked at the network infrastructure supporting electricity grids in this research.

The research try to implements an intelligent network operation management system for enterprise networks. First, based on Flask-state software architecture, the system adapt to Phytium CPU and Galaxy Kylin operating system successfully. Second, using the Isolation Forest algorithm, the system implements the anomaly detection of host data such as CPU usage. Third, using the Holt-winters seasonal prediction model, the system can predict time series data such as network I/O. The results show that the system can realizes anomaly detection and time series data prediction more precisely and intelligently.

Public key cryptography plays an important role in secure communications over insecure channels. Elliptic curve cryptography, as a variant of public key cryptography, has been extensively used in the last decades for such purposes. In this paper, we present a software tool for parallel generation of cryptographic keys based on elliptic curves. Binary method for point multiplication and C++ threads were used in parallel implementation, while secp256k1 elliptic curve was used for testing. Obtained results show speedup of 30% over the sequential solution for 8 threads. The results are briefly discussed in the paper.

In the operation of information technology (IT) services, operators monitor the equipment-issued alarms, to locate the cause of a failure and take action. Alarms generate simultaneously from multiple devices with physical/logical connections. Therefore, if the time and location of the alarms are close to each other, it can be judged that the alarms are likely to be caused by the same event. In this paper, we propose a method that takes a novel approach by correlating alarms considering event units using a Bayesian network based on alarm generation time, generation place, and alarm type. The topology information becomes a critical decision element when doing the alarm correlation. However, errors may occur when topology information updates manually during failures or construction. Therefore, we show that event-by-event correlation with 100% accuracy is possible even if the topology information is 25% wrong by taking into location information other than topology information.

A single RFID (Radio Frequency Identification) is a technology for the remote identification of objects or people. It integrates a reader that receives the information contained in an RFID tag through an RFID authentication protocol. RFID provides several security services to protect the data transmitted between the tag and the reader. However, these advantages do not prevent an attacker to access this communication and remaining various security and privacy issues in these systems. Furthermore, with the rapid growth of IoT, there is an urgent need of security authentication and confidential data protection. Authentication protocols based on elliptic curve cryptographic (ECC) were widely investigated and implemented to guarantee protection against the various attacks that can suffer an RFID system. In this paper, we are going to focus on a comparative study between the most efficient ECC-based RFID authentication protocols that are already published, and study their security against the different wireless attacks.

Recent years have witnessed impressive advances in technology which led to the rapid growth of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) using numerous low-powered devices with a huge number of actuators and sensors. These devices gather and exchange data over the internet and generate enormous amounts of data needed to be secured. Although traditional cryptography provides an efficient means of addressing device and communication confidentiality, integrity, and authenticity issues, it may not be appropriate for very resource-constrained systems, particularly for end-nodes such as a simply connected sensor. Thus, there is an ascent need to use lightweight cryptography (LWC) providing the needed level of security with less complexity, area and energy overhead. In this paper, four lightweight cryptographic algorithms called PRESENT, LED, Piccolo, and SPARX were implemented over a Contiki-based IoT operating system, dedicated for IoT platforms, and assessed regarding RAM and ROM usage, power and energy consumption, and CPU cycles number. The Cooja network simulator is used in this study to determine the best lightweight algorithms to use in IoT applications utilizing wireless sensor networks technology.

The use of software daily has become inevitable nowadays. Almost all everyday tools and the most different areas (e.g., medicine or telecommunications) are dependent on software. The C programming language is one of the most used languages for software development, such as operating systems, drivers, embedded systems, and industrial products. Even with the appearance of new languages, it remains one of the most used [1] . At the same time, C lacks verification mechanisms, like array boundaries, leaving the entire responsibility to the developer for the correct management of memory and resources. These weaknesses are at the root of buffer overflows (BO) vulnerabilities, which range the first place in the CWE’s top 25 of the most dangerous weaknesses [2] . The exploitation of BO when existing in critical safety systems, such as railways and autonomous cars, can have catastrophic effects for manufacturers or endanger human lives.

State of the art Artificial Intelligence Assurance (AIA) methods validate AI systems based on predefined goals and standards, are applied within a given domain, and are designed for a specific AI algorithm. Existing works do not provide information on assuring subjective AI goals such as fairness and trustworthiness. Other assurance goals are frequently required in an intelligent deployment, including explainability, safety, and security. Accordingly, issues such as value loading, generalization, context, and scalability arise; however, achieving multiple assurance goals without major trade-offs is generally deemed an unattainable task. In this manuscript, we present two AIA pipelines that are model-agnostic, independent of the domain (such as: healthcare, energy, banking), and provide scores for AIA goals including explainability, safety, and security. The two pipelines: Adversarial Logging Scoring Pipeline (ALSP) and Requirements Feedback Scoring Pipeline (RFSP) are scalable and tested with multiple use cases, such as a water distribution network and a telecommunications network, to illustrate their benefits. ALSP optimizes models using a game theory approach and it also logs and scores the actions of an AI model to detect adversarial inputs, and assures the datasets used for training. RFSP identifies the best hyper-parameters using a Bayesian approach and provides assurance scores for subjective goals such as ethical AI using user inputs and statistical assurance measures. Each pipeline has three algorithms that enforce the final assurance scores and other outcomes. Unlike ALSP (which is a parallel process), RFSP is user-driven and its actions are sequential. Data are collected for experimentation; the results of both pipelines are presented and contrasted.

SDN represents a significant advance for the telecom world, since the decoupling of the control and data planes offers numerous advantages in terms of management dynamism and programmability, mainly due to its software-based centralized control. Unfortunately, these features can be exploited by malicious entities, who take advantage of the centralized control to extend the scope and consequences of their attacks. When this happens, both the legal and network technical fields are concerned with gathering information that will lead them to the root cause of the problem. Although forensics and incident response processes share their interest in the event information, both operate in isolation due to the conceptual and pragmatic challenges of integrating them into SDN environments, which impacts on the resources and time required for information analysis. Given these limitations, the current work focuses on proposing a framework for SDNs that combines the above approaches to optimize the resources to deliver evidence, incorporate incident response activation mechanisms, and generate assumptions about the possible origin of the security problem.

Emails are widely used as a form of communication and sharing files in an organization. However, email is widely used by cybercriminals to spread malware and carrying out cyber-attacks. We implemented an open-source email gateway in conjunction with a security sandbox for securing emails against malicious attachments. The email gateway scans all incoming and outgoing emails and stops emails containing suspicious files. An automated python script would then send the suspected email to the sandboxing element through sandbox API for further analysis, while the script is used also for the prevention of duplicate results. Moreover, the mail server administrator receives notifications from the email gateway about suspicious attachments. If detected attachment is a true positive based on the sandbox analysis result, email is deleted, otherwise, the email is delivered to the recipient. The paper describes in an empirical way the steps followed during the implementation, results, and conclusions of our research.

Security in the communication systems rely mainly on a trusted Public Key Infrastructure (PKI) and Certificate Authorities (CAs). Besides the lack of automation, the complexity and the cost of assigning a signed certificate to a device, several allegations against CAs have been discovered, which has created trust issues in adopting this standard model for secure systems. The automation of the servers certificate assignment was achieved by the Automated Certificate Management Environment (ACME) method, but without confirming the trust of assigned certificate. This paper presents a complete tested and implemented solution to solve the trust of the Certificates provided to the servers by using the blockchain platform for certificate validation. The Blockchain network provides an immutable data store, holding the public keys of all domain names, while resolving the trust concerns by applying an automated Blockchain-based Domain Control Validation (B-DCV) for the server and client server verification. The evaluation was performed on the Ethereum Rinkeby testnet adopting the Proof of Authority (PoA) consensus algorithm which is an improved version of Proof of Stake (Po \$S\$) applied on Ethereum 2.0 providing superior performance compared to Ethereum 1.0.

The security proposed for Vehicle-to-Everything (V2X) systems in the European Union is specified in the ETSI Cooperative Intelligent Transport System (C-ITS) standards, and related documents are based on the trusted PKI/CAs. The C-ITS trust model platform comprises an EU Root CA and additional Root CAs run in Europe by member state authorities or private organizations offering certificates to individual users. A new method is described in this paper where the security in V2X is based on the Distributed Public Keystore (DPK) platform developed for Ethereum blockchain. The V2X security is considered as one application of the DPK platform. The DPK stores and distributes the vehicles, RSUs, or other C-ITS role-players’ public keys. It establishes a generic key exchange/ agreement scheme that provides mutual key, entity authentication, and distributing a session key between two peers. V2X communication based on this scheme can establish an end-to-end (e2e) secure session and enables vehicle authentication without the need for a vehicle certificate signed by a trusted Certificate Authority.

The article describes an analytical model of the actions of an information security violator for the secret extraction of confidential information processed on the protected object in terms of the theory of Markov random processes. The characteristics of the existing models are given, as well as the requirements that are imposed on the model for simulating the process. All model states are described in detail, as well as the data flow that is used in the process simulation. The model is represented as a directed state graph. It also describes the option for evaluating the data obtained during modeling. In the modern world, with the developing methods and means of covert extraction of information, the problem of assessing the damage that can be caused by the theft of the organization's data is acute. This model can be used to build a model of information security threats.

The progression of cryptographic attacks in the ICT era doubtless leads to the development of new cryptographic algorithms and assessment, and evaluation of the existing ones. In this paper, the artificial intelligence application, through the fuzzy analytic hierarchy process (FAHP) implementation, is used to rank criteria and sub-criteria on which the algorithms are based to determine the most promising criteria and optimize their use. Out of fifteen criteria, security soundness, robustness and hardware failure distinguished as significant ones.

The fifth-generation (5G) mobile telecom network has been garnering interest in both academia and industry, with better flexibility and higher performance compared to previous generations. Along with functionality improvements, new attack vectors also made way. Network operators and regulatory organizations wish to have a more precise idea about the security posture of 5G environments. Meanwhile, various security metrics for IT environments have been around and attracted the community’s attention. However, 5G-specific factors are less taken into consideration.This paper considers such 5G-specific factors to identify potential gaps if existing security metrics are to be applied to the 5G environments. In light of the layered nature and multi-ownership, the paper proposes a new approach to the modular computation of security metrics based on cross-layer projection as a means of information sharing between layers. Finally, the proposed approach is evaluated through simulation.

The paper considers the issue of assessing threats to information security in industrial automation and telecommunication systems in order to improve the efficiency of their security systems. A method for determining a quantitative indicator of threats is proposed, taking into account the probabilistic nature of the process of implementing negative impacts on objects of both industrial and telecommunications systems. The factors that contribute and (or) initiate them are also determined, the dependences of the formal definition of the quantitative indicator of threats are obtained. Methods for a quantitative threat assessment as well as the degree of this threat are presented in the form of a mathematical model in order to substantiate and describe the method for determining a threat to industrial automation systems. Recommendations necessary for obtaining expert assessments of negative impacts on the informatisation objects and information security systems counteracting are formulated to facilitate making decisions on the protection of industrial and telecommunication systems.

Higher education is increasingly called upon to enhance cyber education, including hands-on "experiential" training. The good news is that additional tools and techniques are becoming more available, both in-house and through third parties, to provide cyber training environments and simulations at various features and price points. However, the training thus far has only focused on "traditional" Cybersecurity that lightly touches on wireless in undergraduate and master's degree programs, and certifications. The purpose of this research is to identify and recognize nascent cyber training emphasizing a broader spectrum of wireless security and encourage curricular development that includes critical experiential training. Experiential wireless security training is important to keep pace with the growth in wireless communication mediums and associated Internet of Things (IoT) and Cyber Physical System (CPS) applications. Cyber faculty at a university offering undergraduate and master's Cybersecurity degrees authored this paper; both degrees are offered to resident as well as online students.

In this paper, we show that caching can aid in achieving secure communications by considering a wiretap scenario where the transmitter and legitimate receiver share access to a secure cache, and an eavesdropper is able to tap transmissions over a binary erasure wiretap channel during the delivery phase of a caching protocol. The scenario under consideration gives rise to a new channel model for wiretap coding that allows the transmitter to effectively choose a subset of bits to erase at the eavesdropper by caching the bits ahead of time. The eavesdropper observes the remainder of the coded bits through the wiretap channel for the general case. In the wiretap type-II scenario, the eavesdropper is able to choose a set of revealed bits only from the subset of bits not cached. We present a coding approach that allows efficient use of the cache to realize a caching gain in the network, and show how to use the cache to optimize the information theoretic security in the choice of a finite blocklength code and the choice of the cached bit set. To our knowledge, this is the first work on explicit algorithms for secrecy coding in any type of caching network.

MIMO technology has been widely used in the telecommunication systems nowadays, and the space-time coding is a key part of MIMO technology. A good coding scheme can exploit the spatial diversity to correct the error which is generated in transmission, and increase the normalized transfer rate with low decoding complexity. On the Basis of the research on different Space-Time Block Codes, this essay proposes a new STBC, Diagonal Block Orthogonal Space-Time Block Code. Then we will compare it with other STBCs in the performance of bit error rate, transfer rate, decoding complexity and peek-to-average power ratio, the final result will prove the superiority of DBOAST.

In the course of the research, the research of discriminatory methods of handling video information resource based on the JPEG platform was carried out. This research showed a high interest of the scientific world in identifying important data at different phases of handling. However, the discriminatory handling of the video information resource after the quantization phase is not well understood. Based on the research data, the goal is to find possible ways to operation a video information resource based on a JPEG platform in order to identify important data in a telecommunications system. At the same time, the proposed strategies must provide the required pace of dynamic picture grade and hiding in the context of limited bandwidth. The fulfillment of the condition with limited bandwidth is achieved through the use of a lossless compression algorism based on arithmetic coding. The purpose of the study is considered to be achieved if the following requirements are met:1.Reduction of the volume of dynamic pictures by 30% compared to the initial amount;2.The quality pace is confirmed by an estimate of the peak signal-to-noise ratio for an authorized user, which is Ψauthor ≥ 20 dB;3.The pace of hiding is confirmed by an estimate of the peak signal-to-noise ratio for unauthorized access, which is Ψunauthor ≤ 9 dBThe first strategy is to use encryption tables. The advantage of this strategy is its high hiding strength.The second strategy is the important matrix method. The advantage of this strategy is higher performance.Thus, the goal of the study on the development of possible ways of handling a video information resource based on a JPEG platform in order to identify important data in a telecommunication system with the given requirements is achieved.

With the gradually popular high-speed wireless networks and 5G environments, the quality and reliability of network services will be suited for mobile vehicles. In addition to communicating information between vehicles, they can also communicate information with surrounding roadside equipment, pedestrians or traffic signs, and thus improve the road safety of passers-by.Recently, various countries have continuously invested in research on autonomous driving and unmanned vehicles. The open communication environment of the Internet of Vehicles in 5G will expose all personal information in the field of wireless networks. This research is based on the consideration of information security and personal data protection. We will focus on how to protect the real-time transmission of information between mobile vehicles to prevent from imbedding or altering important transmission information by unauthorized vehicles, drivers or passers-by participating in communications. Moreover, this research proposes a multi-level security key management agreement based on a dynamic M-tree structure for Internet of Vehicles to achieve flexible and scalable key management on large-scale Internet of Vehicles.

Intrusion detection systems installed on the information security devices that control the internal and external perimeter of the demilitarized zones are not able to detect the vulnerability of ZeroLogon after the successful penetration of the intruder into the zone. Component solution for ZeroLogon control is offered. The paper presents the research results of the capabilities for built-in Active Directory audit mechanisms and open source intrusion detection/prevention systems, which allow identification of the critical vulnerability CVE-2020-1472. These features can be used to improve the quality of cyber-physical systems management, to perform audits, as well as to check corporate domains for ZeroLogon vulnerabilities.

Digital watermarking is the multimedia leading security protection as it permanently escorts the digital content. Image copyright protection is becoming more anxious as the new 5G technology emerged. Protecting images with a robust scheme without distorting them is the main trade-off in digital watermarking. In this paper, a watermarking scheme based on discrete cosine transform (DCT) and singular value decomposition (SVD) using canny edge detector technique is proposed. A binary encrypted watermark is reshaped into a vector and inserted into the edge detected vector from the diagonal matrix of the SVD of DCT DC and low-frequency coefficients. Watermark insertion is performed by using an edge-tracing mechanism. The scheme is evaluated using the Peak Signal to Noise Ratio (PSNR) and Normalized Correlation (NC). Attained results are competitive when compared to present works in the field. Results show that the PSNR values vary from 51 dB to 55 dB.

With the processes of collecting, analyzing, and transmitting the data in the Internet of Things (IoT), the Internet of Medical Things (IoMT) comprises the medical equipment and applications connected to the healthcare system and offers an entity with real time, remote measurement, and analysis of healthcare data. However, the IoMT ecosystem deals with some great challenges in terms of security, such as privacy leaking, eavesdropping, unauthorized access, delayed detection of life-threatening episodes, and so forth. All these negative effects seriously impede the implementation of the IoMT ecosystem. To overcome these obstacles, this article presents an efficient, outsourced online/offline revocable ciphertext policy attribute-based encryption scheme with the aid of cloud servers and blockchains in the IoMT ecosystem. Our proposal achieves the characteristics of fine-grained access control, fast encryption, outsourced decryption, user revocation, and ciphertext verification. It is noteworthy that based on the chameleon hash function, we construct the private key of the data user with collision resistance, semantically secure, and key-exposure free to achieve revocation. To the best of our knowledge, this is the first protocol for a revocation mechanism by means of the chameleon hash function. Through formal analysis, it is proven to be secure in a selectively replayable chosen-ciphertext attack (RCCA) game. Finally, this scheme is implemented with the Java pairing-based cryptography library, and the simulation results demonstrate that it enables high efficiency and practicality, as well as strong reliability for the IoMT ecosystem.

The latest security methods are based on biometric features. The electrocardiogram is increasingly used in such systems because it provides biometric features that are difficult to falsify. This paper aims to study the use of the electrocardiogram together with the Convolutional Neural Networks, in order to identify the subjects based on the ECG signal and to improve the security. In this study, we used the Fantasia database, available on the PhysioNet platform, which contains 40 ECG recordings. The ECG signal is pre-processed, and then spectrograms are generated for each ECG signal. Spectrograms are applied to the input of several architectures of Convolutional Neural Networks like Inception-v3, Xception, MobileNet and NasNetLarge. An analysis of performance metrics reveals that the subject identification method based on ECG signal and CNNs provides remarkable results. The best accuracy value is 99.5% and is obtained for Inception-v3.